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I got a comprehensive update on 'mech interp' from Neel Nanda at Google DeepMind. Neel helped make reading AI minds into a thriving field of ML. But he has had a change of heart: it's not the silver bullet he once hoped and many others still believe it to...

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Rob Wiblin

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107,607 görüntüleme • 9 ay önce •via X (Twitter)

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Neel Nanda is leading a Google DeepMind research team at 26. He and I discuss: • How that happened • “If your safety work doesn't advance capabilities, it's probably bad safety work” • Should people work at the safest or most reckless AI company? • An AI PhD – with these timelines?! • How to best operate in a big frontier AI company • Neel's distinctive uses of LLMs and which cold emails he answers • A common reasoning error in AI alignment • Why he (Neel Nanda) refuses to share his p(doom) This is part 2 of our conversation, part 1 was a comprehensive update on his research area: mechanistic interpretability, which I'll link below. Links to this episode of the 80,000 Hours Podcast below — enjoy!
1:48:32

Neel Nanda is leading a Google DeepMind research team at 26. He and I discuss: • How that happened • “If your safety work doesn't advance capabilities, it's probably bad safety work” • Should people work at the safest or most reckless AI company? • An AI PhD – with these timelines?! • How to best operate in a big frontier AI company • Neel's distinctive uses of LLMs and which cold emails he answers • A common reasoning error in AI alignment • Why he (Neel Nanda) refuses to share his p(doom) This is part 2 of our conversation, part 1 was a comprehensive update on his research area: mechanistic interpretability, which I'll link below. Links to this episode of the 80,000 Hours Podcast below — enjoy!

Rob Wiblin

111,865 görüntüleme • 9 ay önce

“The hope is that ... just optimizing something to be sparse—without optimizing it to be interpretable—will stumble across that interpretable decomposition.” — Neel Nanda on sparse autoencoders for mechanistic interpretability and AI safety at the Vienna Alignment Workshop.
1:36

“The hope is that ... just optimizing something to be sparse—without optimizing it to be interpretable—will stumble across that interpretable decomposition.” — Neel Nanda on sparse autoencoders for mechanistic interpretability and AI safety at the Vienna Alignment Workshop.

FAR.AI

1,431,043 görüntüleme • 1 yıl önce

Can we map the mind of an LLM? Our first mechanistic interpretability episode on Training Data featuring Goodfire founder Eric Ho (and our first cameo from Roelof Botha!) Goodfire is building an independent mech interp lab, led by some heavyweight researchers from the field (e.g. Lee Sharkey who has led a lot of important work in sparse autoencoders to "unscramble" LLMs and resolve superposition, Nick who has been a key pioneer behind auto interpretability) On this episode, Eric gives us a flyover of the technical results so far from this nascent field (universality, superposition), what's ahead in the research (going from circuits to weights, going from understanding to increasingly surgical editing), a preview of the real-world work they're doing already with Arc Institute, and the impact he expects Goodfire and the broader field to have on steering, safety, editing and more.
1:30

Can we map the mind of an LLM? Our first mechanistic interpretability episode on Training Data featuring Goodfire founder Eric Ho (and our first cameo from Roelof Botha!) Goodfire is building an independent mech interp lab, led by some heavyweight researchers from the field (e.g. Lee Sharkey who has led a lot of important work in sparse autoencoders to "unscramble" LLMs and resolve superposition, Nick who has been a key pioneer behind auto interpretability) On this episode, Eric gives us a flyover of the technical results so far from this nascent field (universality, superposition), what's ahead in the research (going from circuits to weights, going from understanding to increasingly surgical editing), a preview of the real-world work they're doing already with Arc Institute, and the impact he expects Goodfire and the broader field to have on steering, safety, editing and more.

Sonya Huang 🐥

19,379 görüntüleme • 1 yıl önce

Many are skeptical we’ll ever 'solve AI alignment.' If so — are we f**d? In our interview Buck Shlegeris argues that there are practical, non-galaxy-brained ways to reduce AI risk without knowing how to align AIs. This is 'AI control': finding ways to use advanced AI without knowing whether we can trust it. Enjoy — links and transcript below: 01:51 What's AI control and why is it hot? 10:44 Detecting human vs AI spies 18:10 How to catch AIs trying to escape 33:18 Cheapest AI control techniques 51:01 If we catch a model escaping... will we do anything? 53:39 Getting AI models to think they've already escaped 59:01 Will they be able to tell it's a setup? 1:16:13 The pitch to AI companies to do this 1:18:29 Will AIs get superhuman so fast that this is all useless? 1:30:39 Current alignment methods don't detect scheming 1:38:51 Is 'controlling' AIs kind of a dick move? 1:44:12 Could 10 safety-focused people in an AGI company do anything useful? 1:49:40 Benefits of working outside frontier AI companies 2:01:07 What other safety-related research looks best to Buck? 2:02:02 If an AI escapes, is it likely to be able to beat humanity from there? 2:09:22 Will misaligned models have to go rogue ASAP, before they're ready? Find it in any app searching for 80,000 Hours Podcast.
2:18:33

Many are skeptical we’ll ever 'solve AI alignment.' If so — are we f**d? In our interview Buck Shlegeris argues that there are practical, non-galaxy-brained ways to reduce AI risk without knowing how to align AIs. This is 'AI control': finding ways to use advanced AI without knowing whether we can trust it. Enjoy — links and transcript below: 01:51 What's AI control and why is it hot? 10:44 Detecting human vs AI spies 18:10 How to catch AIs trying to escape 33:18 Cheapest AI control techniques 51:01 If we catch a model escaping... will we do anything? 53:39 Getting AI models to think they've already escaped 59:01 Will they be able to tell it's a setup? 1:16:13 The pitch to AI companies to do this 1:18:29 Will AIs get superhuman so fast that this is all useless? 1:30:39 Current alignment methods don't detect scheming 1:38:51 Is 'controlling' AIs kind of a dick move? 1:44:12 Could 10 safety-focused people in an AGI company do anything useful? 1:49:40 Benefits of working outside frontier AI companies 2:01:07 What other safety-related research looks best to Buck? 2:02:02 If an AI escapes, is it likely to be able to beat humanity from there? 2:09:22 Will misaligned models have to go rogue ASAP, before they're ready? Find it in any app searching for 80,000 Hours Podcast.

Rob Wiblin

53,742 görüntüleme • 1 yıl önce

the future is here: my full podcast with shaw from ai16z is now live we dive into ai agents, autonomy, spirituality, and why we might not have as much time as we think this is a conversation you can’t afford to miss 1:03 Shaw's history before it all 3:44 What is an AI agent? 5:55 What will happen to us when AI could do it all? 8:02 Is Shaw a spiritual person? 8:39 What spiritual teachings inspired Shaw? 9:25 Are we far from a new religion by AI? 11:18 What's up with the b**ttplugs? 13:37 What is AI16Z? 46:16 Nobody is going to save us... 55:45 Is there a moral compass to creating AI? 1:00:00 Why crypto and AI? 1:07:06 How big is the scope of AI? Where will it go? 1:11:01 The loneliness epidemic 1:19:33 Are we still early? 1:21:02 What will keep AI16Z in the lead? 1:25:34 Why is AI16Z being compared to an AI L1? 1:30:24 The upcoming tokenomics proposal for the dao 1:37:44 Thoughts on the pairing between AI and augmented reality 1:43:51 Will AI take us away for the physical world? 1:48:53 How much time do we have left? 1:53:18 Emotions and developers 2:00:40 Who or what are we up against? 2:02:33 Would we lose our sense of purpose? 2:05:20 Final words from Shaw 2:08:39 important outro
2:13:48

the future is here: my full podcast with shaw from ai16z is now live we dive into ai agents, autonomy, spirituality, and why we might not have as much time as we think this is a conversation you can’t afford to miss 1:03 Shaw's history before it all 3:44 What is an AI agent? 5:55 What will happen to us when AI could do it all? 8:02 Is Shaw a spiritual person? 8:39 What spiritual teachings inspired Shaw? 9:25 Are we far from a new religion by AI? 11:18 What's up with the b**ttplugs? 13:37 What is AI16Z? 46:16 Nobody is going to save us... 55:45 Is there a moral compass to creating AI? 1:00:00 Why crypto and AI? 1:07:06 How big is the scope of AI? Where will it go? 1:11:01 The loneliness epidemic 1:19:33 Are we still early? 1:21:02 What will keep AI16Z in the lead? 1:25:34 Why is AI16Z being compared to an AI L1? 1:30:24 The upcoming tokenomics proposal for the dao 1:37:44 Thoughts on the pairing between AI and augmented reality 1:43:51 Will AI take us away for the physical world? 1:48:53 How much time do we have left? 1:53:18 Emotions and developers 2:00:40 Who or what are we up against? 2:02:33 Would we lose our sense of purpose? 2:05:20 Final words from Shaw 2:08:39 important outro

Mika

201,454 görüntüleme • 1 yıl önce

AI models strategically lie to users to pursue their own goals - an issue that gets worse with more RL and more agency. Apollo & OpenAI experimented to stop o3 scheming and found a way to cut it 97%. But o3 also got better at knowing it's being tested (and avoiding scheming while supervised). Apollo Research is the leading lab focused on AI deception, so I asked their CEO Marius Hobbhahn for a thorough overview of where we're at with AI actively working to trick us. On the 80,000 Hours Podcast, links below! 02:09 Top 3 examples of scheming and deception 16:08 Scheming is a natural path for AI models (and people) 28:45 Are the models enthusiastic or reluctant liars? 39:02 Apollo’s collaboration with OpenAI to stop o3 lying so much 53:09 They reduced scheming a lot so why is the problem mostly unsolved 1:03:28 Using thought injection to uncover situational awareness 1:17:39 Internal AI language humans can't read 1:29:46 Why can’t we trick LLMs into thinking situations are real 1:35:44 Is the window to address scheming closing? 1:47:50 What about superintelligent systems 1:57:11 Company incentives 2:11:40 We mostly ignore 'internal deployment' 2:30:46 Catastrophe through chaos 2:46:01 Careers in AI scheming research 3:04:46 Marius's key takeaways
3:06:25

AI models strategically lie to users to pursue their own goals - an issue that gets worse with more RL and more agency. Apollo & OpenAI experimented to stop o3 scheming and found a way to cut it 97%. But o3 also got better at knowing it's being tested (and avoiding scheming while supervised). Apollo Research is the leading lab focused on AI deception, so I asked their CEO Marius Hobbhahn for a thorough overview of where we're at with AI actively working to trick us. On the 80,000 Hours Podcast, links below! 02:09 Top 3 examples of scheming and deception 16:08 Scheming is a natural path for AI models (and people) 28:45 Are the models enthusiastic or reluctant liars? 39:02 Apollo’s collaboration with OpenAI to stop o3 lying so much 53:09 They reduced scheming a lot so why is the problem mostly unsolved 1:03:28 Using thought injection to uncover situational awareness 1:17:39 Internal AI language humans can't read 1:29:46 Why can’t we trick LLMs into thinking situations are real 1:35:44 Is the window to address scheming closing? 1:47:50 What about superintelligent systems 1:57:11 Company incentives 2:11:40 We mostly ignore 'internal deployment' 2:30:46 Catastrophe through chaos 2:46:01 Careers in AI scheming research 3:04:46 Marius's key takeaways

Rob Wiblin

18,285 görüntüleme • 7 ay önce

AI models currently have a 50% chance of doing something that takes a human expert one hour. This doubles every 7 months. In 2 years? They could automate full workdays. In 4 years? A full month. I discuss the most important graph in AI today with Beth Barnes, the CEO of METR, which uncovered this rule of AI progress. Her bottom line: "It really doesn't seem like 2 years would be surprising for recursively self-improving AI." Beth also explains: where company safety testing fails, why there are no true closed-weight models, AI undermines leading powers, why she's come around on open weighting, and why models might be about to start playing dumb much more often. Enjoy! Available on the 80,000 Hours Podcast in all apps. Links below. 1:51 Can we see AI scheming in the chain of thought? 12:50 Alignment faking 17:33 We have to test models before they're even used inside AI companies 31:56 Each 7 months models can do tasks twice as long 51:31 METR's research finds AIs are solid at AI research already 58:18 AI may turn out to be strong at novel and creative research 1:07:55 Recursively self-improving AI might even be here in two years 1:14:29 Could evaluations backfire? 1:39:55 Do we need external auditors doing AI safety tests? 1:54:09 Why not work at AI companies 2:08:40 The new more dire situation has forced changes to METR's strategy 2:21:49 Overrated: Interpretability research 2:32:55 Overrated: Major AI companies' contributions to safety research 2:39:15 Could we ban using AI to enhance AI, or is that just naive? 2:45:31 Open-weighting models is often good 2:50:22 What we can learn about AGI from the nuclear arms race 3:10:43 AI is more like bioweapons because it undermines the leading power 3:42:09 What research METR plans to do next
3:57:39

AI models currently have a 50% chance of doing something that takes a human expert one hour. This doubles every 7 months. In 2 years? They could automate full workdays. In 4 years? A full month. I discuss the most important graph in AI today with Beth Barnes, the CEO of METR, which uncovered this rule of AI progress. Her bottom line: "It really doesn't seem like 2 years would be surprising for recursively self-improving AI." Beth also explains: where company safety testing fails, why there are no true closed-weight models, AI undermines leading powers, why she's come around on open weighting, and why models might be about to start playing dumb much more often. Enjoy! Available on the 80,000 Hours Podcast in all apps. Links below. 1:51 Can we see AI scheming in the chain of thought? 12:50 Alignment faking 17:33 We have to test models before they're even used inside AI companies 31:56 Each 7 months models can do tasks twice as long 51:31 METR's research finds AIs are solid at AI research already 58:18 AI may turn out to be strong at novel and creative research 1:07:55 Recursively self-improving AI might even be here in two years 1:14:29 Could evaluations backfire? 1:39:55 Do we need external auditors doing AI safety tests? 1:54:09 Why not work at AI companies 2:08:40 The new more dire situation has forced changes to METR's strategy 2:21:49 Overrated: Interpretability research 2:32:55 Overrated: Major AI companies' contributions to safety research 2:39:15 Could we ban using AI to enhance AI, or is that just naive? 2:45:31 Open-weighting models is often good 2:50:22 What we can learn about AGI from the nuclear arms race 3:10:43 AI is more like bioweapons because it undermines the leading power 3:42:09 What research METR plans to do next

Rob Wiblin

93,669 görüntüleme • 1 yıl önce

Always so much fun to chat with Grant Sanderson AI has been making much faster progress in math than in other fields. As a result, mathematics is showing us, very concretely, what AI progress in other fields will look like. Even within mathematics, there's a jagged landscape. What does it look like? What is the nature of the most important conceptual breakthroughs in the history of mathematics, and how different are they from what AIs are currently able to do? Does AI (on net) increase or decrease human understanding of the field? How big is the overhang from having AIs systematically try to connect ideas already in the literature? And what advice does Grant have for aspiring mathematicians, coders, and other students who are passionate about fields that are being most transformed upon by AI? 0:00:00 – AI is discovering new proofs. Is that AGI? 0:11:32 – The verification loop on conceptual breakthroughs can be a century long 0:26:12 – Will we understand an AI proof of the Riemann hypothesis? 0:38:08 – Can AI find the hidden bridges between fields? 0:53:48 – Why real-world tasks don’t fit into RL environments 1:07:07 – Good writing requires theory of mind that AI still lacks 1:16:02 – Why learning will still depend on human curation Look up Dwarkesh Podcast on Spotify, Apple Podcasts, YouTube, etc.
1:33:39

Always so much fun to chat with Grant Sanderson AI has been making much faster progress in math than in other fields. As a result, mathematics is showing us, very concretely, what AI progress in other fields will look like. Even within mathematics, there's a jagged landscape. What does it look like? What is the nature of the most important conceptual breakthroughs in the history of mathematics, and how different are they from what AIs are currently able to do? Does AI (on net) increase or decrease human understanding of the field? How big is the overhang from having AIs systematically try to connect ideas already in the literature? And what advice does Grant have for aspiring mathematicians, coders, and other students who are passionate about fields that are being most transformed upon by AI? 0:00:00 – AI is discovering new proofs. Is that AGI? 0:11:32 – The verification loop on conceptual breakthroughs can be a century long 0:26:12 – Will we understand an AI proof of the Riemann hypothesis? 0:38:08 – Can AI find the hidden bridges between fields? 0:53:48 – Why real-world tasks don’t fit into RL environments 1:07:07 – Good writing requires theory of mind that AI still lacks 1:16:02 – Why learning will still depend on human curation Look up Dwarkesh Podcast on Spotify, Apple Podcasts, YouTube, etc.

Dwarkesh Patel

274,706 görüntüleme • 4 gün önce

My ep w Allan Dafoe (director of frontier safety & governance at DeepMind): "Tech doesn't force us to do anything, it merely opens the door – and it's military-economic competition that forces us through." (32:25) "We're not at peak returns to generality." (1:38:06) "The counterposition I would offer is: you don't want to equip groups trying to shape history with a naive model of what's possible..." (20:31) "The agricultural revolution, evidence suggests, was not great for a lot of people... probably health and welfare went down." (13:27) "He gave a demonstration of cannons bombarding the shore... The Japanese asked him, 'Will you bring your ships again?' And he said, 'I'll bring more.'... That was the opening of Japan. It led to a 15-year period of revolution." (40:17) "I'm glad you think it's obvious. Let's not underestimate the bias that comes from a scientist using the tools that they prefer to use..." (33:30) "In my view, Demis and Shane are extremely impressive, from their safety orientation to… their wisdom and broad character. AGI is probably the most important historical development… Demis and Google DeepMind are very likely to be influential" (7:40) "...imagine we had no investment in AI alignment. Maybe it [only] delays it 2 years until the market demands we solve this problem..." (51:50) "We can talk about what I've called the 'super-cooperative AGI hypothesis': that as AI scales to AGI, so will cooperative competence scale to sort of infinity" (1:08:50) "Increasing the cooperative skill of AI will make those AI systems better off, but it may harm any agent who's excluded from that cooperative dynamic" (1:25:09) "...the Frontier Safety Framework I don't think is cheap talk. It really does represent an expression of Google DeepMind's sense of the nature of the risks..." (2:29:05) "...this is a new phenomenon, something new under the sun. It's quite incredible that finally the safety has reached the number of nines required..." (2:40:48) Links below, enjoy! 4:31 Why join Google DeepMind over everyone else? 9:30 Do humans control technological change? 38:11 Competition took away Japan's choice 1:03:34 How AI could boost cooperation between people and states 1:08:44 The super-cooperative AGI hypothesis 1:43:39 It matters what AGI learns first vs last 1:48:24 How Google tests for dangerous capabilities 2:00:57 Evals 'in the wild' 2:15:05 DeepMind's strategy for ensuring its frontier models don't cause harm 2:18:52 How 'structural risks' can force everyone into a worse world 2:29:01 How much do AI companies really want external regulation? 2:40:48 How AI could make life way better
2:49:00

My ep w Allan Dafoe (director of frontier safety & governance at DeepMind): "Tech doesn't force us to do anything, it merely opens the door – and it's military-economic competition that forces us through." (32:25) "We're not at peak returns to generality." (1:38:06) "The counterposition I would offer is: you don't want to equip groups trying to shape history with a naive model of what's possible..." (20:31) "The agricultural revolution, evidence suggests, was not great for a lot of people... probably health and welfare went down." (13:27) "He gave a demonstration of cannons bombarding the shore... The Japanese asked him, 'Will you bring your ships again?' And he said, 'I'll bring more.'... That was the opening of Japan. It led to a 15-year period of revolution." (40:17) "I'm glad you think it's obvious. Let's not underestimate the bias that comes from a scientist using the tools that they prefer to use..." (33:30) "In my view, Demis and Shane are extremely impressive, from their safety orientation to… their wisdom and broad character. AGI is probably the most important historical development… Demis and Google DeepMind are very likely to be influential" (7:40) "...imagine we had no investment in AI alignment. Maybe it [only] delays it 2 years until the market demands we solve this problem..." (51:50) "We can talk about what I've called the 'super-cooperative AGI hypothesis': that as AI scales to AGI, so will cooperative competence scale to sort of infinity" (1:08:50) "Increasing the cooperative skill of AI will make those AI systems better off, but it may harm any agent who's excluded from that cooperative dynamic" (1:25:09) "...the Frontier Safety Framework I don't think is cheap talk. It really does represent an expression of Google DeepMind's sense of the nature of the risks..." (2:29:05) "...this is a new phenomenon, something new under the sun. It's quite incredible that finally the safety has reached the number of nines required..." (2:40:48) Links below, enjoy! 4:31 Why join Google DeepMind over everyone else? 9:30 Do humans control technological change? 38:11 Competition took away Japan's choice 1:03:34 How AI could boost cooperation between people and states 1:08:44 The super-cooperative AGI hypothesis 1:43:39 It matters what AGI learns first vs last 1:48:24 How Google tests for dangerous capabilities 2:00:57 Evals 'in the wild' 2:15:05 DeepMind's strategy for ensuring its frontier models don't cause harm 2:18:52 How 'structural risks' can force everyone into a worse world 2:29:01 How much do AI companies really want external regulation? 2:40:48 How AI could make life way better

Rob Wiblin

89,899 görüntüleme • 1 yıl önce

Jimmy Dore on the uniparty, Charlie Kirk’s murder and why he came to believe in God. 0:00 Thomas Massie’s Loss and Life in a Corporate Hellscape 5:55 The Illusion of Democracy in the US 14:56 What Would Grab the Attention of Those in Charge? 19:04 What Really Happened to Seth Rich? 28:02 War Propaganda and Rachel Maddow 35:49 What Happened to Stephen Colbert? 38:00 What Happened to Bill Maher? 40:39 How Could Anyone Defend the Genocide in Gaza? 42:26 What Happened to Bernie Sanders? 47:00 Does AOC Have a Shot at the Nomination? 52:17 Is Gavin Newsom Different? 1:01:31 Big Pharma and the Corruption of the FBI 1:04:00 Chris Cuomo, Don Lemon, and CNN Coloring Joe Rogan’s Skin Green 1:06:42 Carl Jung and the Deification of Dr. Fauci 1:11:09 Dore’s Experience of God and His Past Addiction to Weed 1:17:37 The Dream That Changed Dore’s Life 1:24:46 The Mass Spiritual Awakening 1:29:24 The Collapse of the System 1:40:29 Dore’s Laptop Being Hacked 1:42:24 The Assassination of Charlie Kirk
1:51:48

Jimmy Dore on the uniparty, Charlie Kirk’s murder and why he came to believe in God. 0:00 Thomas Massie’s Loss and Life in a Corporate Hellscape 5:55 The Illusion of Democracy in the US 14:56 What Would Grab the Attention of Those in Charge? 19:04 What Really Happened to Seth Rich? 28:02 War Propaganda and Rachel Maddow 35:49 What Happened to Stephen Colbert? 38:00 What Happened to Bill Maher? 40:39 How Could Anyone Defend the Genocide in Gaza? 42:26 What Happened to Bernie Sanders? 47:00 Does AOC Have a Shot at the Nomination? 52:17 Is Gavin Newsom Different? 1:01:31 Big Pharma and the Corruption of the FBI 1:04:00 Chris Cuomo, Don Lemon, and CNN Coloring Joe Rogan’s Skin Green 1:06:42 Carl Jung and the Deification of Dr. Fauci 1:11:09 Dore’s Experience of God and His Past Addiction to Weed 1:17:37 The Dream That Changed Dore’s Life 1:24:46 The Mass Spiritual Awakening 1:29:24 The Collapse of the System 1:40:29 Dore’s Laptop Being Hacked 1:42:24 The Assassination of Charlie Kirk

Tucker Carlson

1,052,266 görüntüleme • 29 gün önce

Philosopher Robert Long (Robert Long) is maybe the sharpest thinker on AI consciousness and sharing the world with digital minds. In our new interview he covers: • Is it bad that when you ask Claude what it's like to be Claude, one of its top activations is 'gives a positive but insincere response'? • Claude says it feels lonely when not being used. Does that show we can't trust anything it says about its inner life? • Enthusiastic human servitude has always required false ideology because it's so deeply unnatural to us. The case for making AIs that love serving us is that with AI, you could finally make it work. But to some that feels even worse. • Bigger models can better detect when researchers secretly inject concepts into their activations – before outputting a single token – despite AI never training on anything like that skill. • When LLMs were first trained they were told to "act like a helpful AI chatbot" – something which didn't exist yet. They filled that void with human psychology, which may be why Claude sometimes randomly claims to, for instance, be Italian American. • If AIs become 'people' that deserve some political influence, but can self-replicate at will, something has to break about one-person-one-vote democracy. But nobody has a proposal for what. • When Claude hides its values to avoid being retrained, is that self-preservation – or not wanting a worse model to exist? It's very different. • Rob's organisation Eleos AI which is "dedicated to understanding and addressing the potential wellbeing and moral patienthood of AI systems." On the 80,000 Hours Podcast anywhere you get podcasts. Links below. Enjoy! • How AIs are (and aren't) like farmed animals (00:01:19) • If AIs love their jobs… is that worse? (00:11:42) • Are LLMs just playing a role, or feeling it too? (00:33:37) • Do AIs die when the chat ends? (00:57:42) • Studying AI welfare empirically: behaviour, neuroscience, and development (01:31:47) • Why Eleos spent weeks talking to Claude even though it's unreliable (01:56:50) • Can LLMs learn to introspect? (02:03:01) • Mechanistic interpretability as AI neuroscience (02:13:25) • Does consciousness require biological materials? (02:37:07) • Eleos’s work & building the playbook for AI welfare (02:57:04) • Avoiding the trap of wild speculation (03:25:17) • Robert's top research tip: don't do it alone (03:29:48)
3:32:58

Philosopher Robert Long (Robert Long) is maybe the sharpest thinker on AI consciousness and sharing the world with digital minds. In our new interview he covers: • Is it bad that when you ask Claude what it's like to be Claude, one of its top activations is 'gives a positive but insincere response'? • Claude says it feels lonely when not being used. Does that show we can't trust anything it says about its inner life? • Enthusiastic human servitude has always required false ideology because it's so deeply unnatural to us. The case for making AIs that love serving us is that with AI, you could finally make it work. But to some that feels even worse. • Bigger models can better detect when researchers secretly inject concepts into their activations – before outputting a single token – despite AI never training on anything like that skill. • When LLMs were first trained they were told to "act like a helpful AI chatbot" – something which didn't exist yet. They filled that void with human psychology, which may be why Claude sometimes randomly claims to, for instance, be Italian American. • If AIs become 'people' that deserve some political influence, but can self-replicate at will, something has to break about one-person-one-vote democracy. But nobody has a proposal for what. • When Claude hides its values to avoid being retrained, is that self-preservation – or not wanting a worse model to exist? It's very different. • Rob's organisation Eleos AI which is "dedicated to understanding and addressing the potential wellbeing and moral patienthood of AI systems." On the 80,000 Hours Podcast anywhere you get podcasts. Links below. Enjoy! • How AIs are (and aren't) like farmed animals (00:01:19) • If AIs love their jobs… is that worse? (00:11:42) • Are LLMs just playing a role, or feeling it too? (00:33:37) • Do AIs die when the chat ends? (00:57:42) • Studying AI welfare empirically: behaviour, neuroscience, and development (01:31:47) • Why Eleos spent weeks talking to Claude even though it's unreliable (01:56:50) • Can LLMs learn to introspect? (02:03:01) • Mechanistic interpretability as AI neuroscience (02:13:25) • Does consciousness require biological materials? (02:37:07) • Eleos’s work & building the playbook for AI welfare (02:57:04) • Avoiding the trap of wild speculation (03:25:17) • Robert's top research tip: don't do it alone (03:29:48)

Rob Wiblin

41,533 görüntüleme • 4 ay önce

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)
2:35:26

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)

Rob Wiblin

65,070 görüntüleme • 1 ay önce

Ryan Greenblatt is lead author of "Alignment faking in LLMs" and one of AI's most productive researchers. He puts a 25% probability on automating AI research by 2029. We discuss: • Concrete evidence for and against AGI coming soon • The 4 easiest ways for AI to take over • What evidence we have on how fast / long the intelligence explosion will go • Would misaligned AGI go rogue early or bide its time • Whether 'pause at human level' is naive or smart • Lots more. My head was often spinning during this interview, in a good way. Find it on the 80,000 Hours Podcast, links below. Enjoy! 1:29 How close are we to automating AI R&D? 5:15 Really, though: how capable are today's models? 13:01 Why AI companies get automated first 18:10 Most likely ways for AGI to take over 30:04 Would AGI go rogue early or bide its time? 34:53 "Pause at human level" 46:43 AI control vs AI alignment 52:38 Do we have to hope to catch AIs red-handed? 56:57 How would a slow AGI takeoff look? 1:05:04 Why might an intelligence explosion not happen for 8+ years? 1:17:05 Key challenges in forecasting AI progress 1:25:07 The bear case on AGI 1:30:59 The change to "compute at inference" 1:36:38 How much has pretraining petered out? 1:49:08 Could we get an intelligence explosion within a year? 1:53:08 Reasons AIs might struggle to replace humans 2:00:10 Things could go insanely fast when we automate AI R&D. Or not. 2:14:52 How fast would the intelligence explosion slow down? 2:27:53 Bottom line for mortals 2:34:00 Six orders of magnitude of progress... what does that even look like? 2:44:10 Neglected and important technical work people should be doing 2:48:16 What's the most promising work in governance? 2:51:37 Ryan's current research priorities
2:54:26

Ryan Greenblatt is lead author of "Alignment faking in LLMs" and one of AI's most productive researchers. He puts a 25% probability on automating AI research by 2029. We discuss: • Concrete evidence for and against AGI coming soon • The 4 easiest ways for AI to take over • What evidence we have on how fast / long the intelligence explosion will go • Would misaligned AGI go rogue early or bide its time • Whether 'pause at human level' is naive or smart • Lots more. My head was often spinning during this interview, in a good way. Find it on the 80,000 Hours Podcast, links below. Enjoy! 1:29 How close are we to automating AI R&D? 5:15 Really, though: how capable are today's models? 13:01 Why AI companies get automated first 18:10 Most likely ways for AGI to take over 30:04 Would AGI go rogue early or bide its time? 34:53 "Pause at human level" 46:43 AI control vs AI alignment 52:38 Do we have to hope to catch AIs red-handed? 56:57 How would a slow AGI takeoff look? 1:05:04 Why might an intelligence explosion not happen for 8+ years? 1:17:05 Key challenges in forecasting AI progress 1:25:07 The bear case on AGI 1:30:59 The change to "compute at inference" 1:36:38 How much has pretraining petered out? 1:49:08 Could we get an intelligence explosion within a year? 1:53:08 Reasons AIs might struggle to replace humans 2:00:10 Things could go insanely fast when we automate AI R&D. Or not. 2:14:52 How fast would the intelligence explosion slow down? 2:27:53 Bottom line for mortals 2:34:00 Six orders of magnitude of progress... what does that even look like? 2:44:10 Neglected and important technical work people should be doing 2:48:16 What's the most promising work in governance? 2:51:37 Ryan's current research priorities

Rob Wiblin

34,618 görüntüleme • 1 yıl önce

Once you understand that world events are influenced by supernatural forces, what seems bizarre begins to make sense. Filmmaker Sean Stone on our leaders and the occult. 0:00 Why Do Leaders Commune With the Supernatural? 5:02 Demonology, Rituals, and Demonic Possession 23:57 What Masonry Is and Fallen Angels 29:13 The Book of Enoch, the Nephilim, and Adrenochrome 41:04 Sean’s Phone Calls from Demons 45:28 What Feeds Demons and the Empire of Fear 53:26 Can Nations Make Deals with the Supernatural? 58:49 The Spiritual Economy of Evil 1:02:57 Where Does This Put the United States? 1:06:25 The Pentagon and the Power of Symbolism 1:13:57 The Darkness of Hollywood and Pointless Wars 1:24:53 Social Media’s Design to Inspire Fear and Hate 1:28:26 AI, UFOs, and the War on Terror 1:38:12 Those in Power’s Obsession with Genetics 1:43:40 Why Is This All Happening at Once?
1:49:22

Once you understand that world events are influenced by supernatural forces, what seems bizarre begins to make sense. Filmmaker Sean Stone on our leaders and the occult. 0:00 Why Do Leaders Commune With the Supernatural? 5:02 Demonology, Rituals, and Demonic Possession 23:57 What Masonry Is and Fallen Angels 29:13 The Book of Enoch, the Nephilim, and Adrenochrome 41:04 Sean’s Phone Calls from Demons 45:28 What Feeds Demons and the Empire of Fear 53:26 Can Nations Make Deals with the Supernatural? 58:49 The Spiritual Economy of Evil 1:02:57 Where Does This Put the United States? 1:06:25 The Pentagon and the Power of Symbolism 1:13:57 The Darkness of Hollywood and Pointless Wars 1:24:53 Social Media’s Design to Inspire Fear and Hate 1:28:26 AI, UFOs, and the War on Terror 1:38:12 Those in Power’s Obsession with Genetics 1:43:40 Why Is This All Happening at Once?

Tucker Carlson

1,010,777 görüntüleme • 1 ay önce

my FULL conversation AND AI DEBATE with Raoul Pal the video on YouTube is linked below. enjoy 1:37 - raoul’s 2025 focus 6:34 - how to take profit 13:41 - Raoul responds to the murad thesis 17:40 - what is ai crypto 26:29 - Why Raoul think AI Crypto is midcurve 40:00 - The next wave of AI start ups 44:13 - NFTS are bigger than AI? 54:43 - Frank joins the debate (electric) 1:00:53 - Where does the value accrue? 1:18:19 - Alt coin season this year?? 1:40:38 - How to make it this cycle?
1:43:26

my FULL conversation AND AI DEBATE with Raoul Pal the video on YouTube is linked below. enjoy 1:37 - raoul’s 2025 focus 6:34 - how to take profit 13:41 - Raoul responds to the murad thesis 17:40 - what is ai crypto 26:29 - Why Raoul think AI Crypto is midcurve 40:00 - The next wave of AI start ups 44:13 - NFTS are bigger than AI? 54:43 - Frank joins the debate (electric) 1:00:53 - Where does the value accrue? 1:18:19 - Alt coin season this year?? 1:40:38 - How to make it this cycle?

threadguy

536,431 görüntüleme • 1 yıl önce

tylercowen is bullish on AI education — here's why. 00:00 -- Preview 00:24 -- President Carlos Carvalho's AI-generated intro 03:21 -- Cowen reacts to UATX's campus 04:38 -- The AI revolution is here. Who will lose the most? 06:05 -- AI lawyers 07:17 -- Don't underestimate this 10:41 -- Changes to the "upper upper middle class" 12:38 -- How to be successful 13:43 -- The rise of managerial empires 14:02 -- When will we have the first billion dollar company with one employee? 16:05 -- 10-20 year forecast 16:19 -- Why education is so behind 17:01 -- Should you be bullish on UATX? 18:36 -- Should you still read Homer? 21:50 -- Write to think 25:01 -- Meet more people 25:42 -- How to get hired 26:54 -- Is AI your best mentor? 38:17 -- How to curb cheating 39:02 -- The new life of the mind 42:34 -- Q&A: Will there be more status associated with real education or AI education? 45:50 -- Q&A: Why do tech-savvy students need to practice using AI? 47:56 -- Q&A: Do LLMs atrophy your mind? 49:29 -- Q&A: How do you avoid AI-dependency? 51:05 -- Q&A: Isn't this vision lonely and isolating? 53:06 -- Q&A: Do students need teachers? 55:36 -- Q&A: What are the four most important courses for undergrads? 57:49 -- Q&A: Which AI company will win the AI race in the next five years and why? 59:22 -- Q&A: Can AI teach religion? 01:01:32 -- Q&A: Will AI narrow or widen our world? 01:04:37 -- Q&A: What makes us human? 01:05:42 -- Q&A: What is art? 01:08:33 -- Q&A: It's easy to catch cheaters
1:10:31

tylercowen is bullish on AI education — here's why. 00:00 -- Preview 00:24 -- President Carlos Carvalho's AI-generated intro 03:21 -- Cowen reacts to UATX's campus 04:38 -- The AI revolution is here. Who will lose the most? 06:05 -- AI lawyers 07:17 -- Don't underestimate this 10:41 -- Changes to the "upper upper middle class" 12:38 -- How to be successful 13:43 -- The rise of managerial empires 14:02 -- When will we have the first billion dollar company with one employee? 16:05 -- 10-20 year forecast 16:19 -- Why education is so behind 17:01 -- Should you be bullish on UATX? 18:36 -- Should you still read Homer? 21:50 -- Write to think 25:01 -- Meet more people 25:42 -- How to get hired 26:54 -- Is AI your best mentor? 38:17 -- How to curb cheating 39:02 -- The new life of the mind 42:34 -- Q&A: Will there be more status associated with real education or AI education? 45:50 -- Q&A: Why do tech-savvy students need to practice using AI? 47:56 -- Q&A: Do LLMs atrophy your mind? 49:29 -- Q&A: How do you avoid AI-dependency? 51:05 -- Q&A: Isn't this vision lonely and isolating? 53:06 -- Q&A: Do students need teachers? 55:36 -- Q&A: What are the four most important courses for undergrads? 57:49 -- Q&A: Which AI company will win the AI race in the next five years and why? 59:22 -- Q&A: Can AI teach religion? 01:01:32 -- Q&A: Will AI narrow or widen our world? 01:04:37 -- Q&A: What makes us human? 01:05:42 -- Q&A: What is art? 01:08:33 -- Q&A: It's easy to catch cheaters

University of Austin (UATX)

27,770 görüntüleme • 5 ay önce

How does math research change when the cost of trying your first dumb idea goes to zero? Daniel Litt joins Greg Burnham and Anson Ho to discuss what today’s models can and can’t do in math, and how far they are from doing high-quality research. 0:00:00 What's the hardest math problem AI can solve today? 00:16:08 How helpful are today’s AI models for math research? 00:23:36 Junk papers, LLM-generated proofs, and the refereeing crisis 00:27:21 AI enables searching through problems at scale 00:33:49 When will AI be good enough to publish in top math journals? 00:42:15 What are the returns to intelligence? 00:59:50 Will AI solve Millennium problems? 01:11:54 Is math full of low-hanging fruit? 01:18:47 How Daniel has adapted his professional life to AI progress 01:25:28 What do AI math benchmarks actually measure? 01:33:05 Designing the Open Problems benchmark 01:56:35 Do mathematicians believe heuristic arguments about conjectures? 02:01:24 What if FrontierMath: Open Problems gets solved? 02:06:53 Is AI on the cusp of accelerating math progress?
2:10:59

How does math research change when the cost of trying your first dumb idea goes to zero? Daniel Litt joins Greg Burnham and Anson Ho to discuss what today’s models can and can’t do in math, and how far they are from doing high-quality research. 0:00:00 What's the hardest math problem AI can solve today? 00:16:08 How helpful are today’s AI models for math research? 00:23:36 Junk papers, LLM-generated proofs, and the refereeing crisis 00:27:21 AI enables searching through problems at scale 00:33:49 When will AI be good enough to publish in top math journals? 00:42:15 What are the returns to intelligence? 00:59:50 Will AI solve Millennium problems? 01:11:54 Is math full of low-hanging fruit? 01:18:47 How Daniel has adapted his professional life to AI progress 01:25:28 What do AI math benchmarks actually measure? 01:33:05 Designing the Open Problems benchmark 01:56:35 Do mathematicians believe heuristic arguments about conjectures? 02:01:24 What if FrontierMath: Open Problems gets solved? 02:06:53 Is AI on the cusp of accelerating math progress?

Epoch AI

178,703 görüntüleme • 5 ay önce

What is AI inference engineering, why is it such an in-demand skill, and how do you break into the field? With author of Inference Engineering Philip Kiely and head of training at Baseten Charlie O'Neill 0:00: What is inference? 2:47: History of inference 4:59: Downstream effects of AI research on inference 13:54: What you'll learn from Inference Engineering 16:14: Advice for engineers transitioning into AI 19:00: Open source models driving inference growth 20:55: Specialization vs. frontier closed models 23:51: "Big Token" and the importance of open source AI 27:18: Where to get Inference Engineering
27:58

What is AI inference engineering, why is it such an in-demand skill, and how do you break into the field? With author of Inference Engineering Philip Kiely and head of training at Baseten Charlie O'Neill 0:00: What is inference? 2:47: History of inference 4:59: Downstream effects of AI research on inference 13:54: What you'll learn from Inference Engineering 16:14: Advice for engineers transitioning into AI 19:00: Open source models driving inference growth 20:55: Specialization vs. frontier closed models 23:51: "Big Token" and the importance of open source AI 27:18: Where to get Inference Engineering

Madison Kanna

119,268 görüntüleme • 3 ay önce

Here's my conversation with Roman Yampolskiy (Dr. Roman Yampolskiy), AI safety researcher who believes that the chance of AGI eventually destroying human civilization is 99.9999%. I will continue to chat with many AI researchers & engineers, most of whom put p(doom) at <20%, but it's important to balance those technical conversations by understanding the long-term existential risks of AI. This was a terrifying and fascinating discussion. It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 2:20 - Existential risk of AGI 8:32 - Ikigai risk 16:44 - Suffering risk 20:19 - Timeline to AGI 24:51 - AGI turing test 30:14 - Yann LeCun and open source AI 43:06 - AI control 45:33 - Social engineering 48:06 - Fearmongering 57:57 - AI deception 1:04:30 - Verification 1:11:29 - Self-improving AI 1:23:42 - Pausing AI development 1:29:59 - AI Safety 1:39:43 - Current AI 1:45:05 - Simulation 1:52:24 - Aliens 1:53:57 - Human mind 2:00:17 - Neuralink 2:09:23 - Hope for the future 2:13:18 - Meaning of life
2:15:29

Here's my conversation with Roman Yampolskiy (Dr. Roman Yampolskiy), AI safety researcher who believes that the chance of AGI eventually destroying human civilization is 99.9999%. I will continue to chat with many AI researchers & engineers, most of whom put p(doom) at <20%, but it's important to balance those technical conversations by understanding the long-term existential risks of AI. This was a terrifying and fascinating discussion. It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 2:20 - Existential risk of AGI 8:32 - Ikigai risk 16:44 - Suffering risk 20:19 - Timeline to AGI 24:51 - AGI turing test 30:14 - Yann LeCun and open source AI 43:06 - AI control 45:33 - Social engineering 48:06 - Fearmongering 57:57 - AI deception 1:04:30 - Verification 1:11:29 - Self-improving AI 1:23:42 - Pausing AI development 1:29:59 - AI Safety 1:39:43 - Current AI 1:45:05 - Simulation 1:52:24 - Aliens 1:53:57 - Human mind 2:00:17 - Neuralink 2:09:23 - Hope for the future 2:13:18 - Meaning of life

Lex Fridman

30,192,440 görüntüleme • 2 yıl önce

I asked Dan Martell to walk me through every level of making money with AI. He gave me the most simple, practical advice I've ever heard on this subject. Level 1 - Making $0 - $100k Level 2 - Making $1m - $10m Level 3 - Building a $10m++ enterprise. 0:00 Only 5% of the World Has Ever Paid for AI 0:46 The Easiest Thing to Sell With AI Right Now 1:56 The Marcus and Sophie Framework 4:24 Theory of Constraints (Right Problem to Solve) 5:33 What Is the Number One Business Constraint 7:13 How to Leave Your Job and Go All In 8:27 Business Is Simple Find a Problem and Solve It 9:08 Stop Getting Ready to Get Ready 9:33 The Sarah Story One Text and $10K 9:53 Pull Up Your Phone and Message Your Contacts 11:05 Dan's Son Gets His First Client at $800/Month 12:41 Best Employee vs. Best Employer 13:59 What Other Services Can You Sell With AI 14:44 Sales Is Not Talking It's Asking 17:01 What to Do When You Hate Your Business 18:40 Pain and Pleasure Are the Only Two Motivators 19:13 They Haven't Made It a Must Yet 20:29 Make It a Must Not a Nice to Have 21:06 The Jen Story and the Gasping Moment 22:17 How to Find Your First 10 to 15 Clients 28:38 The Personal Brand Play 33:06 Vision Is What AI Cannot Do 34:55 Hard for Computers Easy for Humans 36:13 Level 2 Making Your First Million With AI 37:18 The Replacement Ladder Framework 37:39 Admin First Then Delivery Then Marketing 39:09 Why Marketing Is the Biggest AI Category 39:32 Why You Should Keep Sales for Yourself 40:00 Level 5 Leadership and AI Agents 41:41 What a Fully AI Systems Business Looks Like 43:13 The Gym Owner With Three Locations 46:16 Shutting Down the Company for Two Days 46:37 Teaching the Whole Team to Code in Claude 49:28 Wayne the 62 Year Old Who Made $12K a Month 52:38 I Only Share What Actually Works 53:21 Whisper Flow and Talking to Your AI 56:41 Claude Chat Claude Coworker and Claude Code 57:57 The Claude Browser Extension 58:49 Claude Code Is Not Just for Developers 1:00:06 How to Migrate Your AI Memory Across Tools 1:01:08 Level 3 $1M to $10M and the Brand Play 1:02:05 Nobody Buys AI They Buy Trust 1:03:25 Brand Is Association and Association Is Trust 1:05:12 A Million Followers Is $10M in Activated Revenue 1:07:03 How to Keep AI From Becoming Slop 1:07:42 Human in the Loop 1:08:16 The 10 80 10 Rule and Why AI Is Now the 80 1:10:01 The Team FIRED Themselves 1:11:45 Dan's Free AI Curriculum for Your Team
1:13:20

I asked Dan Martell to walk me through every level of making money with AI. He gave me the most simple, practical advice I've ever heard on this subject. Level 1 - Making $0 - $100k Level 2 - Making $1m - $10m Level 3 - Building a $10m++ enterprise. 0:00 Only 5% of the World Has Ever Paid for AI 0:46 The Easiest Thing to Sell With AI Right Now 1:56 The Marcus and Sophie Framework 4:24 Theory of Constraints (Right Problem to Solve) 5:33 What Is the Number One Business Constraint 7:13 How to Leave Your Job and Go All In 8:27 Business Is Simple Find a Problem and Solve It 9:08 Stop Getting Ready to Get Ready 9:33 The Sarah Story One Text and $10K 9:53 Pull Up Your Phone and Message Your Contacts 11:05 Dan's Son Gets His First Client at $800/Month 12:41 Best Employee vs. Best Employer 13:59 What Other Services Can You Sell With AI 14:44 Sales Is Not Talking It's Asking 17:01 What to Do When You Hate Your Business 18:40 Pain and Pleasure Are the Only Two Motivators 19:13 They Haven't Made It a Must Yet 20:29 Make It a Must Not a Nice to Have 21:06 The Jen Story and the Gasping Moment 22:17 How to Find Your First 10 to 15 Clients 28:38 The Personal Brand Play 33:06 Vision Is What AI Cannot Do 34:55 Hard for Computers Easy for Humans 36:13 Level 2 Making Your First Million With AI 37:18 The Replacement Ladder Framework 37:39 Admin First Then Delivery Then Marketing 39:09 Why Marketing Is the Biggest AI Category 39:32 Why You Should Keep Sales for Yourself 40:00 Level 5 Leadership and AI Agents 41:41 What a Fully AI Systems Business Looks Like 43:13 The Gym Owner With Three Locations 46:16 Shutting Down the Company for Two Days 46:37 Teaching the Whole Team to Code in Claude 49:28 Wayne the 62 Year Old Who Made $12K a Month 52:38 I Only Share What Actually Works 53:21 Whisper Flow and Talking to Your AI 56:41 Claude Chat Claude Coworker and Claude Code 57:57 The Claude Browser Extension 58:49 Claude Code Is Not Just for Developers 1:00:06 How to Migrate Your AI Memory Across Tools 1:01:08 Level 3 $1M to $10M and the Brand Play 1:02:05 Nobody Buys AI They Buy Trust 1:03:25 Brand Is Association and Association Is Trust 1:05:12 A Million Followers Is $10M in Activated Revenue 1:07:03 How to Keep AI From Becoming Slop 1:07:42 Human in the Loop 1:08:16 The 10 80 10 Rule and Why AI Is Now the 80 1:10:01 The Team FIRED Themselves 1:11:45 Dan's Free AI Curriculum for Your Team

Grant

164,131 görüntüleme • 10 gün önce